Challenges

Scientific data collected with modern sensors or dedicated detectors
exceed very often the perimeter of the initial scientific design. These
data are obtained more and more frequently with large material and human
efforts.

A large class of scientific experiments are in fact unique
because of their large scale, with very small chances to be repeated or
superseded by new experiments in the same domain: for instance high
energy physics and astrophysics experiments involve multi-annual and
even multi-decades developments, unlikely repeatable. Other scientific
experiments are in fact unique by nature: earth science, medical
sciences etc. since the collected data is “time-stamped” and thereby
non-reproducible by new experiments or observations.

This new knowledge
obtained using these data (“data observatories”) should be preserved
long term such that the access and the re-use are made possible and lead
to an enhancement of the initial investment. It is therefore of outmost
importance to pursue a coherent and vigorous approach to preserve the
scientific data at long term.

The preservation remains nevertheless a
challlenge due to the complexity of the data structure, the fragility of
the custom-made software environments as well as the lack of rigorous
approaches in workflows and algorithms.